本文在实Hilbert空间中提出了一种求解拟单调变分不等式问题的新的自适应向前向后算法,并在合理的假设下证明了由算法产生的迭代序列强收敛到变分不等式解集的一个元素。最后数值实验阐明了算法的有效性与优越性。
In this paper, we propose a new adaptive forward-backward algorithm to solve the quasimonotone variational inequality problem in real Hilbert spaces. Under reasonable assumptions, we prove that the iterative sequence generated by the algorithm converges strongly to an element of the solution set of the variational inequality problem. Finally, numerical experiments show the effectiveness and superiority of the new algorithm.
[1] Kinderlehrer D, Stampacchia G. An Introduction to Variational Inequalities and Their Applications [M]. New York: Academic Press, 1980.
[2] Kikuchi N, Oden J T. Contact Problems in Elasticity: A Study of Variational Inequalities and Finite Element Methods [M]. Philadelphia: Society for Industrial and Applied Mathematics, 1988.
[3] Facchinei F, Pang J S. Finite-Dimensional Variational Inequalities Problems [M]. New York: Springer, 2003.
[4] Pang J S, Fukushima M. Quasi-variational inequalities, generalized Nash equilibria, and multileader-follower games [J]. Computational Management Science, 2005, 2: 21-56.
[5] Cottle R W, Yao J C. Pseudo-monotone complementarity problems in Hilbert space [J]. Journal of Optimization Theory and Applications, 1992, 75: 281-295.
[6] Ye M L, He Y R. A double projection method for solving variational inequalities without monotonicity [J]. Computational Optimization and Applications, 2015, 60: 141-150.
[7] Korpelevich G M. An extragradient method for finding saddle points and for other problems [J]. Ekonomika I Matematiceskie Metody-Economics and Mathematical Methods, 1976, 12: 747-756.
[8] Tseng P. A modified forward-backward splitting method for maximal monotone mappings [J]. SIAM Journal on Control and Optimization, 2000, 38: 431-446.
[9] Censor Y, Gibali A, Reich S. The subgradient extragradient method for solving variational inequalities in Hilbert spaces [J]. Journal of Optimization Theory and Applications, 2011, 148: 318-335.
[10] Dong Q L, Jiang D, Gibali A. A modified subgradient extragradient method for solving the variational inequality problem [J]. Numerical Algorithms, 2018, 79: 927-940.
[11] Shehu Y, Iyiola O S, Yao J C. New projection methods with inertial steps for variational inequalities [J]. Optimization, 2022, 71: 4731-4762.
[12] Long X J, Yang J, Cho Y J. Modified subgradient extragradient algorithms with a new linesearch rule for variational inequalities [J]. Bulletin of the Malaysian Mathematical Sciences Society, 2023, 46: 140.
[13] Rehman H U, Peng Z Y, Yao J C. Approximate subgradient extragradient methods for solving variational inequality problems: Convergence analysis and applications in signal and image processing [J]. Communications in Nonlinear Science and Numerical Simulation, 2026, 152: 106211.
[14] Izuchukwu C, Shehu Y, Yao J C. New inertial forward-backward type for variational inequalities with quasi-monotonicity [J]. Journal of Global Optimization, 2022, 84: 441-464.
[15] Wang K, Wang Y H, Shehu Y, et al. Double inertial forward-backward-forward method with adaptive step-size for variational inequalities with quasi-monotonicity [J]. Communications in Nonlinear Science and Numerical Simulation, 2024, 132: 107924.
[16] Goebel K, Reich S. Uniform Convexity, Hyperbolic Geometry and Nonexpansive Mapping [M]. New York: Marcet Dekker, 1984.
[17] Saejung S, Yotkaew P. Approximation of zeros of inverse strongly monotone operators in Banach spaces [J]. Nonlinear Analysis, 2012, 75: 742-750.
[18] Maingé P E. Approximation methods for common fixed points of nonexpansive mappings in Hilbert spaces [J]. Journal of Mathematical Analysis and Applications, 2007, 325: 469-479.
[19] Hoai P T. A new proximal gradient method for solving mixed variational inequality problems with a novel explicit stepsize and applications [J]. Mathematics and Computers in Simulation, 2025, 29: 594-610.